Intelligent Transportation Systems

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20 papers in the last four weeks, up 233% on the four weeks before. 0.2% of all new papers.

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MoE Enhanced Federated Learning for Spatiotemporal Prediction
Jun 9, 2026Zhehao Dai, Xiao Han, Zhaolin Deng +4Intelligent Transportation Systems Mixture of Experts
Zhejiang University of Technology, Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou · Jilin University, Jilin · City University of Hong Kong, Hong Kong SAR China
Efficient Traffic Prediction at Scale: A Systematic Study of STGCN Architectural Depth
Jun 8, 2026Soban Nasir Lone, Mohamed Abouelela, Taeyoung Yu +2Efficient Neural Network Inference Intelligent Transportation Systems
Technical University of Munich Munich, Germany · The University of Queensland Brisbane, Australia
Revisiting mesoscopic traffic flow simulation in SUMO: Limitations, analysis, and an alternative
Jun 8, 2026Ying-Chuan Ni, Alina Akopian, Anastasios Kouvelas +1Intelligent Transportation Systems
Traffic Engineering Group, Institute for Transport Planning and Systems, ETH Zurich, Stefano-Franscini-Platz 5, Zürich, 8093, Switzerland
DynaOD: Dynamic Origin-Destination Flow Generation with Discrete-to-Continuous Temporal Semantic Modeling
Jun 8, 2026Jie Zhao, Xianqi Dai, Jie Feng +2Intelligent Transportation Systems Synthetic Data Generation
Department of Electronic Engineering, BNRist, Tsinghua University · Tsinghua Shenzhen International Graduate School · Zhongguancun Academy
A Resilience-as-a-Service assessment framework for coordinated disruption response in interdependent urban transit systems
Jun 7, 2026Sara Jaber, S. M. Hassan Mahdavi, Neila Bhouri +1Intelligent Transportation Systems
Univ. Gustave Eiffel, COSYS, GRETTIA, Paris, France · VEDECOM, mobiLAB, Department of Human factors and Economics of Sustainable Mobility, Versailles, France
Platooning Connected, Autonomous, and Human-Driven Vehicles: A Deep Reinforcement Learning-based Approach
Jun 7, 2026Zhen Qina, Dong-Fan Xie, Heng Ma +2Reinforcement Learning Intelligent Transportation Systems
aSchool of Systems Science, Beijing Jiaotong University, Beijing 100044, China · bFaculty of Science and Engineering, University of Nottingham Ningbo China
Cross-View Urban Traffic Dataset: Drone-Supervised Ground Truth for Monocular Bird's-Eye View Localization
Jun 5, 2026Prakhar Bhardwaj, Simone Weikl, Kilian Mang +1Intelligent Transportation Systems Autonomous Driving Perception
Faculty of Computer Science and Mathematics, OTH Regensburg
An Integrated Roadside Sensing and Communication Framework for Vulnerable Road User Safety at Signalized Intersections
Jun 5, 2026Parvez AnowarIntegrated Sensing and Communication Intelligent Transportation Systems
PhD Student, Department of Civil, Environmental and Construction Engineering, University of Central Florida, 12800 Pegasus Drive, Orlando, FL 32816, United States
DSFNet: Learning Dual-Domain Spectral Operators for Multi-Modality Spatio-Temporal Forecasting in Urban Transportation Systems
Jun 5, 2026Yongchao Li, Yang Li, Zhuoxuan Li +4Intelligent Transportation Systems Time Series Forecasting
School of Transportation, Southeast University, Nanjing 211189, China · Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 211189, China · Department of Data Science, City University of Hong Kong, Hong Kong 999077, China +6
Federated Foundation Models over Vehicular Networks
Jun 5, 2026Kasra Borazjani, Fardis Nadimi, Payam Abdisarabshali +5Multimodal Federated Learning Intelligent Transportation Systems
A Swarm Approach to Public Transit Using On-demand Routing in a Slime-Mold-Inspired Framework
Jun 4, 2026Lindsay Burke, Maxfield Comstock, Jason Graham +3Intelligent Transportation Systems Vehicle Routing
Department of Computer Science, New Jersey Institute of Technology · Federated Department of Biological Sciences, New Jersey Institute of Technology · Department of Mathematics, University of Scranton +1
Coordinated optimization of departure sequencing and section-track allocation in railway short-term concentrated departure scenarios based on qubo and hybrid quantum algorithms
Jun 4, 2026Xiaobin Li, Yanbin Gao, Weiguang Wang +1Intelligent Transportation Systems
School of Transportation Engineering East China Jiaotong University Nanchang, Jiangxi 330000, China
RIDE: An Open Dataset and Benchmark for Train Delay Prediction
Jun 3, 2026Clément Elliker, Mathis Le Bail, Clément Mantoux +2Intelligent Transportation Systems Time Series Forecasting
LIX, École Polytechnique, IP Paris, France · e.SNCF Solutions, France
WHU-Infra3D: A Full-stack Multi-modal Dataset and Benchmark for 3D Roadside Infrastructure Inventory
Jun 3, 2026Chong Liu, Luxuan Fu, Xuyu Feng +23D Point Cloud Segmentation Intelligent Transportation Systems
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning
Jun 2, 2026Dimitris Michailidis, Sennay Ghebreab, Fernando P. SantosReinforcement Learning Intelligent Transportation Systems
Socially Intelligent Artificial Systems University of Amsterdam
Calibrating Urban Traffic Simulation from Sparse Road Observations via Genetic Optimization
Jun 2, 2026Hunter Sawyer, Jesse Roberts, Simon MateiIntelligent Transportation Systems Model Calibration
Department of Computer Science · Tennessee Technological University · Cookeville, TN
PatchSTG: Scalable Spatiotemporal Graph Transformers for Traffic Forecasting on Irregular Sensor Networks
Jun 2, 2026Jichao Li, Xuanming ShiIntelligent Transportation Systems Spatiotemporal Forecasting
Portsmouth Abbey School · CodingFuture (Shanghai) Education Technology Co., Ltd.
A Game-Theoretic Decision Framework for Optimal Selection of Coordination Detection Methods in Multi-UAV Fleet Operations
Jun 1, 2026Christian Manasseh, Savana AmmonsAlgorithm Selection Air Traffic Management
Mobius Logic Inc., Tysons, VA, USA
TrafficRAG: A Multimodal RAG Framework for Traffic Accident Liability Determination
Jun 1, 2026Xu Li, Zedong Fu, Xinyi Li +1Multimodal RAG Intelligent Transportation Systems
Southwest Petroleum University, China · Sichuan Police College, China
Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments
May 31, 2026Dénes Toth, George Ambroladze, Edwin Sundberg +2Intelligent Transportation Systems Active Inference
Department of Computer Systems and Sciences, Stockholm University, Stockholm 164 25, Sweden
AGORA: Can Deliberation and Governance Gates Absorb Participation Bias in Transit Planning?
May 31, 2026Jung-Hoon Cho, Cathy WuIntelligent Transportation Systems Group Fairness
Department of Civil and Environmental Engineering and Laboratory for Information & Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA. · Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA, USA.
Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support
May 31, 2026Siyan Li, Zehao Wang, Jiachen Li +3AI-Assisted Decision Making Intelligent Transportation Systems
Bourns College of Engineering, Center for Environmental Research and Technology, University of California at Riverside, CA, USA · Bourns College of Engineering, Computer Science and Engineering Department, University of California at Riverside, CA, USA · Bourns College of Engineering, Electrical and Computer Engineering Department, University of California at Riverside, CA, USA
Spatiotemporal Multi-Task Graph Transformer for Trip-Level Transit Prediction
May 30, 2026Oluwaleke Yusuf, Adil Rasheed, Frank LindsethIntelligent Transportation Systems Multi-Task Learning
Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim NO-7491, Norway · Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim NO-7491, Norway
MATraM: A Multi-Activity Transport and Mobility Agent-Based Model for Activity Modifications
May 28, 2026Yahya Gamal, Ricardo Colasanti, Gary Polhill +3Intelligent Transportation Systems Agent-Based Modeling
AI for Collective Intelligence Hub University of Glasgow, UK · School of Geography University of Leeds, UK · Information & Computational Sciences Department The James Hutton Institute, UK +1
From GPS Points to Travel Patterns: Flexible and Semantic Trajectory Generation with LLMs
May 28, 2026Silin Zhou, Chenhao Wang, Yuntao Wen +3Trajectory Generation Intelligent Transportation Systems
University of Electronic Science and Technology of China Chengdu, China · King Abdullah University of Science and Technology Thuwal, Saudi Arabia
From XXLTraffic to EvoXXLTraffic: Scaling Traffic Forecasting to Sensor-Evolving Networks
May 28, 2026Du Yin, Hao Xue, Arian Prabowo +2Intelligent Transportation Systems Forecasting Benchmarks
University of New South Wales, Sydney, NSW, Australia
Momentum Based Reward Design for Low Emission Traffic Signal Control
May 28, 2026Chinmay Mundane, Amith Manoharan, Arun Kumar SinghReinforcement Learning Reward Shaping
Institute of Technology, University of Tartu
SCOPE: A Lightweight-training LLM Framework for Air Traffic Control Readback Monitoring
May 28, 2026Qihan Deng, Minghua Zhang, Yang Yang +1Air Traffic Management Intelligent Transportation Systems
Department of Mechanical and Aerospace Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, 999077, Hong Kong · School of Electronic and Information Engineering, Beihang University, Beijing, 100191, China · State Key Laboratory of CNS/ATM, Beijing, 100191, China
Network Optimization Aspects of Autonomous Vehicles: Challenges and Future Directions
May 28, 2026Rudolf Krecht, Tamas Budai, Erno Horvath +3Intelligent Transportation Systems Autonomous Driving
Department of Automation and Mechatronics, Széchenyi István University, Győr, 9026 Hungary · Department of Telecommunications, Széchenyi István University, Győr, 9026 Hungary · Vehicle Industry Development Center, Széchenyi István University, Győr, 9026 Hungary
ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control
May 28, 2026Aoyu Pang, Maonan Wang, Yuejiao Xie +3RL Control Intelligent Transportation Systems
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China · Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong · Shanghai AI Laboratory, Shanghai, China +1

Jun 9, 2026, cs.LGJ/K move · Enter open · S save

MoE Enhanced Federated Learning for Spatiotemporal Prediction

Zhehao Dai, Xiao Han, Zhaolin Deng, Zijian Zhang+3

Zhejiang University of Technology, Zhejiang Key Laboratory of Visual Information Intelligent Processing, Hangzhou · Jilin University, Jilin · City University of Hong Kong, Hong Kong SAR China

Intelligent Transportation Systems Mixture of Experts Cross-Domain Transfer Learning Spatiotemporal Forecasting Federated Learning Heterogeneous Federated Learning

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Traffic prediction is fundamental to intelligent transportation systems and urban computing, yet many cities continue to suffer from traffic data scarcity due to limited sensor deployment and uneven urban development. Cross-city knowledge transfer has thus attracted increasing attention, enabling data-rich cities to assist data-scarce ones. However, centralized approaches raise privacy concerns, while existing federated methods struggle with pronounced spatiotemporal heterogeneity across cities. To address these challenges, we propose MoE-FedTP, a personalized federated cross-city spatiotemporal prediction framework based on lightweight Mixture-of-Experts (MoE) networks. MoE-FedTP first employs spatiotemporal neural networks to extract features from both source and target cities, then introduces a set of expert networks derived from different source cities through partial parameter sharing. A gating mechanism dynamically fuses the experts to capture diverse traffic dynamics, achieving fine-grained modeling of urban heterogeneity while preserving privacy. Experiments on four real-world traffic datasets show that MoE-FedTP consistently outperforms state-of-the-art cross-city and federated learning baselines, demonstrating its effectiveness in enhancing prediction accuracy for data-scarce cities.

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